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A 1-trillion-parameter model needs about 2 TB of memory for its weights at FP16 or BF16, 1 TB at FP8, or 500 GB at 4-bit. Those are decimal-unit estimates for weights alone—not the complete GPU-memory budget. Inference also needs memory for the KV cache, activations, communication, and runtime allocations, so the model must be split across multiple GPUs.
How much memory do the weights require?
Estimate weight storage by multiplying the parameter count by the number of bytes used for each parameter. NVIDIA’s NIM documentation lists the following formats and weight-memory estimates; the totals below are decimal units and exclude overhead and quantization metadata.
| Weight format | Bytes per parameter | Weights for 1 trillion parameters |
|---|---|---|
| FP16 or BF16 | 2 | 2,000,000,000,000 bytes (2 TB) |
| FP8 | 1 | 1,000,000,000,000 bytes (1 TB) |
| INT4 or NVFP4 | 0.5 | 500,000,000,000 bytes (500 GB) |
These figures are a storage floor for model weights, not a promise that a deployment with that much aggregate GPU memory will run successfully. Actual allocations depend on the model and serving configuration. NVIDIA’s NIM memory estimate expresses weight memory per GPU as total parameters multiplied by bytes per parameter, divided by tensor parallelism.
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Even the 4-bit weight estimate is 500 GB, far beyond the memory available on a single GPU. A trillion-parameter model therefore has to be distributed across multiple GPUs. The number required depends on precision, each GPU’s usable memory, and the additional memory needed to serve requests. Simply dividing the weight total by GPU capacity gives only a rough lower bound because it leaves no room for cache or runtime needs.
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NVIDIA illustrates the distinction with a 1.8-trillion-parameter mixture-of-experts model. In that example, the assumed deployment budget is 64 GPUs with 192 GB each; NVIDIA says at least five GPUs are needed just to store the parameters in FP4, while a better user experience needs more. This is a modeled example at 1.8 trillion parameters, not a deployment recipe or minimum GPU count for every 1-trillion-parameter model. NVIDIA’s trillion-scale model discussion also explains that models too large for one GPU must be split and parallelized across GPUs.
What else consumes GPU memory during inference?
Weights are only one part of an inference memory budget. Major additional allocations can include:
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- KV cache: Stores attention keys and values for tokens in active requests. Its size is affected by model architecture, context length, and the number of concurrent users.
- Activations and I/O tensors: Intermediate data used while processing inputs and generating outputs.
- Communication and runtime buffers: Memory for exchanging data across GPUs and for execution features such as CUDA graphs.
- Model-specific allocations: Depending on the deployment, these can include adapters, multimodal reservations, or state used by hybrid models.
TensorRT-LLM’s documentation describes weights, activation tensors, and I/O tensors—especially KV cache—as important memory consumers. Batch size, input length, beam width, token limits, and engine configuration can all change allocations. TensorRT-LLM’s memory documentation explains these configuration-dependent factors.
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Context length and concurrent users change the KV-cache budget
As a model processes longer contexts or more simultaneous requests, KV-cache use can rise substantially. NVIDIA gives one model-specific illustration: about 40 GB of KV cache for a single 128K-token Llama 3 70B request. That figure should not be applied to a different architecture; it shows why weight size by itself cannot determine the total memory requirement. NVIDIA’s Llama 3 memory example notes that cache use in this example scales with the number of users.
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How to estimate a deployment budget
Start with the weight estimate, then check whether the intended GPU configuration has room for serving overhead and the expected workload. A useful estimate needs more than the parameter count:
- Choose the weight precision. Use the table above for the weight-only floor. Quantization can reduce that floor, but the table does not establish a model’s quality at a given precision or account for metadata.
- Set the serving workload. Specify the expected context length, concurrent requests, batch size, and output-token limits. These affect KV-cache and other allocations.
- Account for distribution and communication. Determine how the model will be split across GPUs and whether the interconnect can support the selected parallelism. Tensor parallelism divides work across GPUs, but scaling it without high-bandwidth GPU networking can create communication bottlenecks.
- Reserve runtime headroom. Do not plan to consume all aggregate GPU memory with weights; cache and execution allocations also need space.
For scale, NVIDIA specifies 13.4 TB of HBM3E across all 72 GPUs in its GB200 NVL72 rack system. That is a specification for one particular system, not a general minimum for a trillion-parameter deployment. NVIDIA’s GB200 NVL72 specifications describe that configuration.
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Inference memory is not training memory
The estimates in this article address inference weight storage, not the total memory required to train a model. Training also needs memory for training state and computation. Its total depends on choices such as optimizer, precision policy, sharding, activation handling, sequence length, and batch size, so there is no single universal training-memory multiplier for an unspecified trillion-parameter model.
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